{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:05:36Z","timestamp":1778079936120,"version":"3.51.4"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030147563","type":"print"},{"value":"9783030147570","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-14757-0_5","type":"book-chapter","created":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T02:02:59Z","timestamp":1550714579000},"page":"53-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Challenges in Object Detection Under Rainy Weather Conditions"],"prefix":"10.1007","author":[{"given":"Sinan","family":"Hasirlioglu","sequence":"first","affiliation":[]},{"given":"Andreas","family":"Riener","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,22]]},"reference":[{"key":"5_CR1","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886\u2013893. IEEE (2005)"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1016\/j.trpro.2016.05.233","volume":"14","author":"P Duthon","year":"2016","unstructured":"Duthon, P., Bernardin, F., Chausse, F., Colomb, M.: Methodology used to evaluate computer vision algorithms in adverse weather conditions. Transp. Res. Procedia 14, 2178\u20132187 (2016)","journal-title":"Transp. Res. Procedia"},{"key":"5_CR3","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226\u2013231. AAAI Press (1996)"},{"issue":"6","key":"5_CR4","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"Martin A. Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, vol. 24, pp. 381\u2013395. ACM, New York (1981)","journal-title":"Communications of the ACM"},{"key":"5_CR5","unstructured":"Garg, K., Nayar, S.K.: Photometric model of a rain drop. CMU Technical report (2003)"},{"key":"5_CR6","unstructured":"Garg, K., Nayar, S.K.: Detection and removal of rain from videos. In: Computer Vision and Pattern Recognition (2004)"},{"issue":"1","key":"5_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-006-0028-6","volume":"75","author":"K Garg","year":"2007","unstructured":"Garg, K., Nayar, S.K.: Vision and rain. Int. J. Comput. Vis. 75(1), 3\u201327 (2007)","journal-title":"Int. J. Comput. Vis."},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Gourova, R., Krasnov, O., Yarovoy, A.: Analysis of rain clutter detections in commercial 77 GHz automotive radar. In: 2017 European Radar Conference EURAD, pp. 25\u201328 (2017)","DOI":"10.23919\/EURAD.2017.8249138"},{"issue":"4","key":"5_CR9","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TAES.1983.309350","volume":"AES\u201319","author":"H Rohling","year":"1983","unstructured":"Rohling, H.: Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. Aerosp. Electron. Syst. AES\u201319(4), 608\u2013621 (1983)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"5_CR10","unstructured":"Rohling, H.: Ordered statistic CFAR technique - an overview. In: 2011 12th International Radar Symposium (IRS), pp. 631\u2013638 (2011)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Hasirlioglu, S., Doric, I., Kamann, A., Riener, A.: Reproducible fog simulation for testing automotive surround sensors. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pp. 1\u20137. IEEE (2017)","DOI":"10.1109\/VTCSpring.2017.8108566"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Hasirlioglu, S., Kamann, A., Doric, I., Brandmeier, T.: Test methodology for rain influence on automotive surround sensors. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 2242\u20132247. IEEE (2016)","DOI":"10.1109\/ITSC.2016.7795918"},{"key":"5_CR13","unstructured":"Hassen, A.A.: Indicators for the signal degradation and optimization of automotive radar sensors under adverse weather conditions: Zugl.: Darmstadt, Techn. Univ., Diss., 2006. Berichte aus der Hochfrequenztechnik, Shaker, Aachen (2007)"},{"key":"5_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2012. pp. 1097\u20131105. Curran Associates Inc., USA (2012)"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.5194\/ars-9-49-2011","volume":"9","author":"RH Rasshofer","year":"2011","unstructured":"Rasshofer, R.H., Spies, M., Spies, H.: Influences of weather phenomena on automotive laser radar systems. Adv. Radio Sci. 9, 49\u201360 (2011)","journal-title":"Adv. Radio Sci."},{"key":"5_CR16","unstructured":"Sandner, V.: Development of a test target for AEB systems. In: 23rd International Technical Conference on the Enhanced Safety of Vehicles (ESV): Research Collaboration to Benefit Safety of All Road Users (2013)"},{"issue":"1","key":"5_CR17","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1006\/cviu.1999.0832","volume":"78","author":"P Torr","year":"2000","unstructured":"Torr, P., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138\u2013156 (2000)","journal-title":"Comput. Vis. Image Underst."},{"key":"5_CR18","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001. pp. I-511\u2013I-518. IEEE (2001)"},{"issue":"3","key":"5_CR19","first-page":"183","volume":"22","author":"J Wojtanowski","year":"2014","unstructured":"Wojtanowski, J., Zygmunt, M., Kaszczuk, M., Mierczyk, Z., Muzal, M.: Comparison of 905 nm and 1550 nm semiconductor laser rangefinders\u2019 performance deterioration due to adverse environmental conditions. Opto-Electron. Rev. 22(3), 183\u2013190 (2014)","journal-title":"Opto-Electron. Rev."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Intelligent Transport Systems, From Research and Development to the Market Uptake"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-14757-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T01:32:33Z","timestamp":1558402353000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-14757-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030147563","9783030147570"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-14757-0_5","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"INTSYS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"First International Conference on Intelligent Transport Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimar\u00e3es","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intsys2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/futuretransport.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"http:\/\/confy.eai.eu","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"20","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}